CN115131352B - Corona effect evaluation method for corona machine - Google Patents
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- CBENFWSGALASAD-UHFFFAOYSA-N Ozone Chemical compound [O-][O+]=O CBENFWSGALASAD-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention relates to the technical field of electric data processing, in particular to a corona effect evaluation method of a corona machine; in the method, printed images corresponding to materials before and after corona treatment are respectively obtained by using a pattern recognition method; specifically, the method can adopt related electronic equipment to perform pattern recognition, then perform data processing on data in a printed image to obtain a first gray level difference matrix, a gray level neighborhood difference matrix and a second gray level difference matrix, and calculate a gain effect based on the gray level neighborhood difference matrix; obtaining a weight matrix based on the gray level neighborhood difference matrix; multiplying the weight matrix by a second gray difference matrix to obtain a weighted second gray difference matrix; calculating printing difference; and calculating a performance evaluation value according to the gain effect and the printing difference, and evaluating the corona effect of the corona machine according to the performance evaluation value. The invention adopts a pattern recognition mode to obtain the printing image, and carries out data processing on data in the printing image, thereby being capable of accurately evaluating the corona effect.
Description
Technical Field
The invention relates to the technical field of electric data processing, in particular to a corona effect evaluation method for a corona machine.
Background
The materials such as silicon rubber, plastic metal plates, sheets and the like are not high in surface adhesive force and not strong in coloring capability, so that printed patterns are fuzzy and not high in pattern durability when the materials are directly subjected to printing and dyeing treatment, and the printed patterns are extremely easy to fall off from the surfaces of the materials; therefore, the corona machine is used for carrying out surface treatment on the materials, so that the adhesive force of the surface of the material can be improved, the surface of the material can be coarsened, the surface tension of the material can be improved, and the printing ink, the glue and the coating can be better adhered to the surface of the treated material; meanwhile, ozone generated in the corona process can change the molecular structure of the surface of the material from non-polar to polar. The printing can ensure full color and clean picture for a long time, thereby achieving ideal printing and bonding effects. In the corona treatment process, the corona effect of the corona machine directly influences the quality of later-period printed patterns, if the corona effect does not meet the requirements, the patterns still have the phenomena of fuzziness and low persistence, therefore, in order to ensure the quality of the printed patterns, the corona effect of the corona machine needs to be evaluated, but a relatively mature technical means for evaluating the corona effect of the corona machine does not exist at present.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a corona effect evaluation method of a corona machine, which adopts the following technical scheme:
respectively acquiring corresponding printing images of the material before and after corona treatment; preprocessing the printed image to obtain a gray image;
the pixel values of the corresponding gray level image after the corona treatment and the corresponding position of the corresponding gray level image before the corona treatment are differed to obtain a first gray level difference image; constructing a first gray difference matrix based on the first gray difference image;
randomly selecting one element in the first gray level difference matrix, calculating the difference value between the element and 8 neighborhood elements of the element to obtain a difference value sum, and taking the difference value sum as the gray level neighborhood difference of the element; acquiring gray neighborhood differences corresponding to each element, and constructing a gray neighborhood difference matrix according to the gray neighborhood differences;
calculating the gain effect of corona treatment according to the gray level neighborhood difference matrix;
the pixel value of the corresponding position of the corresponding gray image after the corona treatment and the corresponding position of the gray image corresponding to the standard printing image are subjected to subtraction, and a second gray difference image is obtained; constructing a second gray difference matrix based on the second gray difference image;
performing data processing on each element in the gray level neighborhood difference matrix to obtain a weight matrix;
multiplying the weight matrix by the second gray difference matrix to obtain a weighted second gray difference matrix;
calculating the average value and the variance corresponding to the weighted second gray level difference matrix, and recording the product of the average value and the variance as the printing difference;
and calculating a performance evaluation value according to the gain effect and the printing difference, and evaluating the corona effect of the corona machine based on the performance evaluation value.
Further, the material comprises silicone, rubber or plastic.
Further, the gain effect obtaining method comprises: and calculating the maximum value, the minimum value, the average value and the variance corresponding to the gray level neighborhood difference matrix, and determining the gain effect based on the maximum value, the minimum value, the average value and the variance.
Further, the data processing comprises normalizing each element in the gray neighborhood difference matrix.
Further, the performance evaluation value is the gain effect to print difference ratio.
Further, the method for evaluating the corona effect of the corona machine comprises the following steps: and comparing the performance evaluation value with an evaluation threshold value, wherein when the performance evaluation value is larger than the evaluation threshold value, the corona effect of the corona machine is good, and when the performance evaluation value is smaller than the evaluation threshold value, the corona effect of the corona machine is poor.
The embodiment of the invention at least has the following beneficial effects:
the method comprises the steps of respectively obtaining corresponding printing images of a material before and after corona treatment by using an identification graph; specifically, relevant electronic equipment can be adopted for pattern recognition, data processing is carried out according to the obtained printing image to obtain a first gray level difference matrix, a gray level neighborhood difference matrix and a second gray level difference matrix, and the gain effect of corona treatment is calculated based on the gray level neighborhood difference matrix; obtaining a weight matrix based on the gray level neighborhood difference matrix; multiplying the weight matrix by a second gray difference matrix to obtain a weighted second gray difference matrix; and calculating printing differences; and calculating a performance evaluation value according to the gain effect and the printing difference, and evaluating the corona effect of the corona machine according to the performance evaluation value.
According to the method, corresponding printing images of the material before and after corona treatment are subjected to related data processing, a gray level neighborhood difference matrix is constructed, and the gain effect of the corona treatment is calculated based on the gray level neighborhood difference matrix; the gain effect of corona treatment can be accurately obtained, and the surface adhesive force and the surface tension increased by the material after corona treatment are obtained; meanwhile, the weighted second gray level difference matrix is obtained through the weight matrix and the second gray level difference matrix, so that the gain effect of corona treatment and the printing effect after corona treatment can be represented, the corona treatment effect of a corona machine on a material can be reflected from multiple angles, and the evaluation on the corona treatment effect of the corona machine is more specific and comprehensive.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart illustrating steps of an embodiment of a corona effect evaluation method of a corona machine according to the present invention.
Detailed Description
To further explain the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the proposed solution, its specific implementation, structure, features and effects will be made with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
Referring to fig. 1, a flow chart of steps of a corona effect evaluation method according to an embodiment of the present invention is shown, where the method includes the following steps:
(1) Respectively acquiring corresponding printing images of the material before and after corona treatment; preprocessing a printed image to obtain a gray image, and subtracting the pixel value of the corresponding position of the corresponding gray image after corona treatment from the pixel value of the corresponding position of the corresponding gray image before corona treatment to obtain a first gray difference image; and constructing a first gray difference matrix based on the first gray difference image.
Specifically, the material in the present application includes silicone, rubber or plastic, and the present embodiment takes silicone as an example for description; the method for evaluating the corona effect of the corona machine by using rubber or plastic is consistent with the method for evaluating the corona effect of the corona machine by using silica gel, and is not described in detail.
In the embodiment, the printing operation is directly performed without performing corona treatment on the silica gel to obtain a corresponding printing image of the silica gel before the corona treatment; carrying out corona treatment on the silica gel by using a corona machine, and then carrying out printing operation on the silica gel to obtain a corresponding printing image of the silica gel after corona treatment; during the two printing operations, the printed patterns are consistent.
The method includes the steps that a camera is used for obtaining printing images corresponding to silica gel before and after corona treatment, a component method is adopted for conducting graying treatment on the printing images to obtain grayscale images, furthermore, a histogram equalization algorithm is adopted for conducting image enhancement on the grayscale images, and the contrast of the grayscale images is increased. As another embodiment, the printed image may be subjected to the gradation processing by the maximum value method, the average value method, and the weighted average method.
In this embodiment, a grayscale image corresponding to silica gel before corona treatment is denoted as grayscale image a, a grayscale image corresponding to silica gel after corona treatment is denoted as grayscale image B, and a difference is made between pixel values of corresponding positions of grayscale image a and grayscale image B to obtain a first grayscale difference image
I.e. by
(ii) a Wherein the absolute value is to prevent the gray difference value of the corresponding position from being a negative value; and according to the first gray difference image
And constructing a first gray difference matrix. The size of the grayscale images a and B is equal, m × n, m is the width of the grayscale image, and n is the length of the grayscale image.
The construction rule for constructing the first gray level difference matrix is as follows: taking the pixel value of the 1 st row and the 1 st column in the first gray scale difference image as the element of the 1 st row and the 1 st column in the first gray scale difference matrix, taking the pixel value of the 1 st row and the 2 nd column in the first gray scale difference image as the element of the 1 st row and the 2 nd column in the first gray scale difference matrix, and so on to obtain a first gray scale difference matrix;
that is, the first gray difference matrix is:
in which
Being an element of the 1 st row and 1 st column of the first gray scale difference matrix,
being an element of the 1 st row and the nth column of the first gray scale difference matrix,
is the element of the mth row and the 1 st column of the first gray difference matrix;
is the element of the mth row and the nth column of the first gray scale difference matrix.
It should be noted that the larger the value of each element in the first grayscale difference matrix is, the more obvious the effect of improving the printing quality after corona treatment is; on the contrary, the printing quality improvement effect after the corona treatment is less obvious.
(2) Randomly selecting an element in the first gray level difference matrix, calculating a difference value between the element and an 8-neighborhood element of the element to obtain a difference sum, and taking the difference sum as a gray level neighborhood difference of the element; acquiring gray neighborhood difference corresponding to each element, constructing a gray neighborhood difference matrix according to the gray neighborhood difference, and calculating the gain effect of corona treatment according to the gray neighborhood difference matrix.
Specifically, the difference of the gray neighborhood corresponding to the element in the ith row and the jth column in the first gray difference matrix is as follows:
wherein the content of the first and second substances,
the gray level neighborhood difference corresponding to the element in the ith row and the jth column in the first gray level difference matrix,
is the element in the ith row and the jth column of the first gray scale difference matrix,
is the first in the first gray difference matrix
Go to the first
Elements of a column;
the value set of (a) is { -1,0,1},
is { -1,0,1}, and
and
cannot be 0 at the same time.
It should be noted that, in the calculation of the first gray-scale difference matrix
，
，
，
When the gray level neighborhood of the image is different, the difference value between the gray level neighborhood of the image and the 3 neighborhood elements of the image is calculated to respectively obtain the difference values
，
，
，
Corresponding gray neighborhood difference; similarly, the elements in the 1 st row, 1 st column, the last 1 st row and the last 1 st column of the first gray-scale difference matrix are calculated (excluding the elements
，
，
，
) When the gray scale neighborhood of (1) is different, the difference value with the 5 neighborhood elements is calculated to obtain the elements (excluding the 1 st row, the 1 st column, the last 1 row and the last 1 column) in the first gray scale difference matrix
，
，
，
) Corresponding gray neighborhood difference.
The method for acquiring the intermediate gray level neighborhood difference matrix comprises the following steps: taking the gray neighborhood difference corresponding to the element of the ith row and the jth column in the first gray difference matrix as the element of the ith row and the jth column in the gray neighborhood difference matrix;
namely, the gray neighborhood difference matrix is:
wherein, the first and the second end of the pipe are connected with each other,
is a matrix of the difference of the neighborhood of gray levels,
is the element of the 1 st row and 1 st column of the gray neighborhood disparity matrix,
is the element of the 1 st row and the n th column of the gray neighborhood difference matrix,
the element is the element of the 1 st row of the m-th row of the gray level neighborhood difference matrix;
is the element of the mth row and the nth column of the gray level neighborhood difference matrix.
The reason for constructing the gray neighborhood difference matrix is that the amplitude of the gradient change of the 8 neighborhoods in the first gray neighborhood difference matrix is represented by the gray neighborhood difference matrix; the difference between the two printed images can be represented by a first gray level difference matrix constructed by the corresponding printed image before the silica gel corona treatment and the corresponding printed image after the corona treatment, the larger the difference is, the better the corona treatment effect for representing the corona machine is, and the difference is represented by the size of the value of the element and the fluctuation range, namely the gradient, between the values of the element in the first gray level difference matrix; the gray neighborhood difference matrix is obtained by performing 8 neighborhood processing on elements in the first gray neighborhood difference matrix, so that the value of each element in the gray neighborhood difference matrix represents the gradient change amplitude of the first gray neighborhood difference matrix at the position 8, and the larger the amplitude is, the larger the difference between the printed image corresponding to the silica gel before corona treatment and the printed image corresponding to the silica gel after corona treatment is.
It should be noted that the gray level dependency matrix is constructed according to the number of times of recording gray level dependency in a neighborhood by using 8-neighborhood gradient characteristics, but the gray level dependency matrix cannot express position information, and the construction process is complex, so that in this embodiment, on the basis of constructing the gray level dependency matrix, an eight-neighborhood gradient is used for constructing a gray level neighborhood difference matrix, and the gray level neighborhood difference matrix can record position information of pixels and can express gray level dependency of surrounding pixels; compared with a gray level dependency matrix, the gray level neighborhood difference matrix can represent position information, and meanwhile, the difference between two printed images can be deeply expressed.
The method for acquiring the medium gain effect comprises the following steps: and calculating the maximum value, the minimum value, the average value and the variance corresponding to the gray level neighborhood difference matrix, and determining the gain effect based on the maximum value, the minimum value, the average value and the variance.
The maximum value is:
wherein
Is the maximum value corresponding to the gray neighborhood difference matrix,
as a gray level neighborhood difference matrix，
To find a maximum function; namely, the maximum value corresponding to the gray level neighborhood matrix is the maximum value of the elements in the gray level neighborhood matrix, and the maximum value of the elements represents that the corona treatment gain at the position is maximum.
The minimum value is:
in which
Is the minimum value corresponding to the gray neighborhood difference matrix,
is a matrix of the difference of the neighborhood of gray levels,
to find a minimum function; namely, the minimum value corresponding to the gray level neighborhood matrix is the minimum value of the elements in the gray level neighborhood matrix, and the minimum value of the elements represents the minimum corona treatment gain at the position.
The average values are:
wherein the content of the first and second substances,
is the average value corresponding to the gray neighborhood difference matrix,
the element of the ith row and the jth column of the gray level neighborhood difference matrix;
the total number of rows of the gray level neighborhood difference matrix;
the total column number of the gray level neighborhood difference matrix is obtained;
is the total number of elements in the gray level neighborhood difference matrix. The average value represents the average gain after corona treatment.
The variance is:
wherein the content of the first and second substances,
is the variance corresponding to the gray neighborhood difference matrix,
is the average value corresponding to the gray neighborhood difference matrix,
the element of the ith row and the jth column of the gray level neighborhood difference matrix;
the total number of rows of the gray level neighborhood difference matrix;
the total column number of the gray level neighborhood difference matrix is obtained;
is the total number of elements in the gray neighborhood difference matrix. The variance represents the fluctuation degree of the overall gain after the corona treatment, and the larger the variance is, the larger the fluctuation degree is, and the better the effect of the overall gain is.
The gain effect is as follows:
wherein the content of the first and second substances,
is the average value corresponding to the gray neighborhood difference matrix,
is the maximum value corresponding to the gray neighborhood difference matrix,
the minimum value corresponding to the gray level neighborhood difference matrix is obtained;
the variance corresponding to the gray level neighborhood difference matrix.
According to the common knowledge, the corona treatment effect should be uniform, i.e. the smaller the fluctuation degree of the overall gain, the better the corona treatment effect, the smaller the difference between the maximum value and the minimum value, the better the corona treatment effect, the larger the average gain, the better the corona treatment effect, and based on this, the gain effect after corona treatment is obtained
The larger the gain effect, the higher the quality of the printed pattern corresponding to the corona-treated silicone rubber.
In the embodiment, materials with the same specification are selected, and the same printing operation is carried out on the materials without corona treatment and the materials after corona treatment by using the same printing equipment; obtaining corresponding printing images, and judging the gain effect on the printing operation after the corona treatment according to the difference of the two printing images; the adhesion and the coloring capability added to the material by the corona treatment can be accurately obtained.
(3) The pixel value of the corresponding position of the corresponding gray image after the corona treatment and the corresponding position of the gray image corresponding to the standard printing image are subjected to subtraction, and a second gray difference image is obtained; constructing a second gray difference matrix based on the second gray difference image; processing data of each element in the gray level neighborhood difference matrix to obtain a weight matrix; and multiplying the weight matrix by the second gray level difference matrix to obtain a weighted second gray level difference matrix.
Before a material is printed, a relevant worker will generally design a printed image and a printing effect to obtain a standard printed image; and this standard print image is used to evaluate the print quality of the actual print image.
In this embodiment, the standard printed image is grayed to obtain a grayscale image corresponding to the standard printed image, and the graying process is a known technique and is not described again; and recording the gray level image corresponding to the standard printing image as a gray level image C, and subtracting the pixel values of the corresponding positions of the gray level image B and the gray level image C to obtain a second gray level difference image
I.e. by
(ii) a Wherein the absolute value is to prevent the gray difference value of the corresponding position from being a negative value; and according to the second gray difference image
And constructing a second gray scale difference matrix. The size of the grayscale image B is equal to that of the grayscale image C, the size is m × n, m is the width of the grayscale image, and n is the length of the grayscale image.
The construction rule for constructing the second gray level difference matrix is as follows: taking the pixel value of the 1 st row and the 1 st column in the second gray scale difference image as the element of the 1 st row and the 1 st column in the second gray scale difference matrix, taking the pixel value of the 1 st row and the 2 nd column in the second gray scale difference image as the element of the 1 st row and the 2 nd column in the second gray scale difference matrix, and so on to obtain a second gray scale difference matrix;
that is, the second gray variance matrix is:
wherein, in the process,
being the elements of the 1 st row and 1 st column of the second gray scale difference matrix,
which is an element of the 1 st row and the nth column of the second gray scale difference matrix,
is the element of the mth row and the 1 st column of the second gray scale difference matrix;
is an element of the mth row and nth column of the second gray scale difference matrix. The larger the value of each element in the second gray scale difference matrix, the greater the difference between the corresponding printed image after corona treatment and the standard printed image, and the poorer the quality of the printing.
And further, carrying out data processing on each element in the gray level neighborhood difference matrix to obtain a weight matrix.
The data processing method comprises the following specific steps:
1) Firstly, normalizing each element in a gray level neighborhood difference matrix to obtain each element after normalization, wherein the size sequence of each element after normalization is unchanged;
the normalized elements are:
wherein, the first and the second end of the pipe are connected with each other,
is the normalized element corresponding to the element in the ith row and jth column of the gray neighborhood difference matrix,
is the element of the ith row and the jth column in the gray neighborhood difference matrix,
the total number of rows of the gray level neighborhood difference matrix;
is the total column number of the gray neighborhood difference matrix.
2) Calculating the difference between 1 and the normalized element to obtain each element in the weight matrix, i.e. each element
(ii) a Wherein
Is the element in the ith row and the jth column in the weight matrix.
In this embodiment, the larger the value of the element in the second gray scale difference matrix is, the larger the difference between the corona-treated printed image and the standard printed image at the position is, the worse the printing quality is; in the gray level neighborhood difference matrix at the same position, the larger the value of the element at the position is, the larger the gain of the corona treatment is, and the better the printing quality at the position is; therefore, the gain of the corona treatment is larger, and the weight corresponding to the difference at the position of the second gray level difference matrix is smaller; on the contrary, if the corona gain effect is small, the weight corresponding to the difference at the position of the second gray level difference matrix is large, so the difference value between 1 and the normalized element is used as each element in the weight matrix.
The weight matrix in the above is:
wherein, in the step (A),
as elements of row 1 and column 1 of the weight matrix,
as the element of the weight matrix at row 1 and column n,
the element is the element of the mth row and the 1 st column of the weight matrix;
is the element of the mth row and the nth column of the weight matrix.
Specifically, the calculation formula of the weighted second gray level difference matrix is:
i.e. the weighted second gray level difference matrix is:
(ii) a Wherein, the first and the second end of the pipe are connected with each other,
to weight the elements of the second gray scale difference matrix row 1 and column 1,
to weight the elements of the 1 st row and the n th column of the second gray scale disparity matrix,
to weight the elements of the mth row and 1 st column of the second gray scale difference matrix,
is the element of the mth row and the nth column of the weighted second gray-scale difference matrix;
the calculation formula of (2) is as follows:
in the formula (I), the reaction is carried out,
as elements of row 1 and column 1 of the weight matrix,
is the second gray scale difference momentThe elements in row 1 and column 1 of the matrix, and the formula for weighting the other elements in the second gray level difference matrix
The calculation formulas are similar and are not described in detail. The weighted second gray scale difference matrix can characterize the gain effect of the corona treatment and the print effect after the corona treatment.
It should be noted that the second gray level difference matrix is only used for expressing the printing quality from the difference between the printed image corresponding to the corona-treated material and the standard image, and does not consider the change of the printing effect of the material before and after the corona treatment, in the gray level neighborhood difference matrix, the larger the value of an element is, the better the corona treatment gain effect of the position is represented, that is, the larger the gain of the position after the corona treatment is, the better the printing effect is, and the smaller the gain is, the worse the printing quality is, therefore, the present embodiment performs data processing on each element in the gray level neighborhood difference matrix to obtain the weight matrix. The change of material printing effect around the weight matrix characterization corona treatment, multiply weight matrix and second gray difference matrix, obtain weighted second gray difference matrix, the gain effect of corona treatment not only can be characterized to weighted second gray difference matrix, and the printing effect after the corona treatment can be characterized in addition, embodies the corona treatment effect of corona machine to the material from many angles, makes follow-up evaluation to corona machine corona treatment effect more specifically comprehensive.
(4) And calculating the average value and the variance corresponding to the weighted second gray difference matrix, and recording the product of the variance and the average value as the printing difference.
Specifically, the average value corresponding to the weighted second gray scale difference matrix is:
wherein the content of the first and second substances,
for weighting the ith row of the second gray difference matrixThe elements of the j columns are,
the total row number of the second gray difference matrix is weighted;
the total column number of the weighted second gray scale difference matrix;
is the weighting of the total number of elements in the second gray difference matrix.
The variance corresponding to the weighted second gray level difference matrix is:
wherein the content of the first and second substances,
to weight the elements of the ith row and jth column of the second gray level difference matrix,
to weight the average value corresponding to the second gray scale difference matrix,
the total row number of the second gray difference matrix is weighted;
the total column number of the weighted second gray scale difference matrix;
the total number of elements in the second gray variance matrix is weighted.
The calculation formula of the printing difference is as follows:
wherein, in the process,
to weight the average value corresponding to the second gray scale difference matrix,
the variance corresponding to the second gray difference matrix is weighted.
The larger the variance corresponding to the weighted second gray scale difference matrix is, the more uneven the color of the printed image after corona treatment is, and the worse the printing quality is; the larger the average value corresponding to the weighted second gray scale difference matrix is, the larger the difference between the printed image subjected to the characteristic corona treatment and the standard printed image is, and the worse the printing quality is; the larger the printing difference, the worse the printing quality.
(5) And calculating a performance evaluation value according to the gain effect and the printing difference, and evaluating the corona effect of the corona machine based on the performance evaluation value.
Specifically, the performance evaluation value is a ratio of the gain effect to the printing difference, and is calculated by the following formula:
wherein, in the step (A),
in order to evaluate the value of the performance,
in order to achieve the effect of the gain,
is the print difference.
The method for evaluating the corona effect of the corona machine comprises the following steps: comparing the performance evaluation value with the evaluation threshold value, when the performance evaluation value is larger than the evaluation threshold value, the corona effect of the corona machine is good, and when the performance evaluation value is smaller than the evaluation threshold value, the corona effect of the corona machine is poor, and the expression is as follows:
wherein
To evaluate the threshold, the evaluation threshold was obtained by big data analysis,
is a performance evaluation value.
Meanwhile, the performance evaluation value can also show the working state of the corona machine, and in the actual running process of the corona machine, the performance of the corona machine is possibly influenced by various factors such as the increase of the service time, the change of the environmental temperature, the aging of certain components and the like, so that the treatment effect of the corona machine is increasingly poor, and therefore whether the working state of the corona machine is normal or not is judged through the performance evaluation value, namely when the performance evaluation value is larger than an evaluation threshold value and the corona effect is good, the working state of the corona machine is judged to be normal at the moment, and when the performance evaluation value is smaller than the evaluation threshold value and the corona effect is poor, the working state of the corona machine is judged to be abnormal at the moment and needs to be overhauled.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (5)
1. A corona effect evaluation method of a corona machine is characterized by comprising the following steps:
respectively acquiring corresponding printing images of the material before and after corona treatment; preprocessing the printed image to obtain a gray image;
the pixel values of the corresponding gray level image after the corona treatment and the corresponding position of the corresponding gray level image before the corona treatment are differed to obtain a first gray level difference image; constructing a first gray difference matrix based on the first gray difference image;
randomly selecting an element in the first gray level difference matrix, calculating a difference value between the element and an 8-neighborhood element of the element to obtain a difference sum, and taking the difference sum as a gray level neighborhood difference of the element; acquiring gray neighborhood differences corresponding to each element, and constructing a gray neighborhood difference matrix according to the gray neighborhood differences;
calculating the gain effect of corona treatment according to the gray level neighborhood difference matrix;
the gain effect obtaining method comprises the following steps: calculating the maximum value, the minimum value, the average value and the variance corresponding to the gray level neighborhood difference matrix, and determining the gain effect based on the maximum value, the minimum value, the average value and the variance;
the maximum value is:
wherein
Is the maximum value corresponding to the gray neighborhood difference matrix,
is a matrix of the difference of the neighborhood of gray levels,
to find a maximum function; the maximum value corresponding to the gray level neighborhood matrix is the maximum value of elements in the gray level neighborhood matrix;
the minimum value is:
wherein
Is the minimum value corresponding to the gray neighborhood difference matrix,
is a matrix of the difference of the neighborhood of gray levels,
to find a minimum function; the minimum value corresponding to the gray level neighborhood matrix is the minimum value of elements in the gray level neighborhood matrix;
the average values are:
wherein, the first and the second end of the pipe are connected with each other,
is the average value corresponding to the gray neighborhood difference matrix,
the element of the ith row and the jth column of the gray level neighborhood difference matrix;
the total number of rows of the gray level neighborhood difference matrix;
the total column number of the gray level neighborhood difference matrix is obtained;
the total number of elements in the gray level neighborhood difference matrix;
the variance is:
wherein, the first and the second end of the pipe are connected with each other,
is the variance corresponding to the gray neighborhood difference matrix,
is the average value corresponding to the gray neighborhood difference matrix,
the element of the ith row and the jth column of the gray level neighborhood difference matrix;
the total number of rows of the gray level neighborhood difference matrix;
the total column number of the gray level neighborhood difference matrix is obtained;
the total number of elements in the gray level neighborhood difference matrix;
the gain effect is as follows:
wherein the content of the first and second substances,
is the average value corresponding to the gray neighborhood difference matrix,
is the maximum value corresponding to the gray neighborhood difference matrix,
the minimum value corresponding to the gray level neighborhood difference matrix is obtained;
the variance corresponding to the gray level neighborhood difference matrix;
the pixel value of the corresponding position of the corresponding gray level image after corona treatment and the corresponding position of the gray level image corresponding to the standard printing image are subjected to difference, and a second gray level difference image is obtained; constructing a second gray difference matrix based on the second gray difference image;
performing data processing on each element in the gray neighborhood difference matrix to obtain a weight matrix;
multiplying the weight matrix by the second gray difference matrix to obtain a weighted second gray difference matrix;
calculating the average value and the variance corresponding to the weighted second gray level difference matrix, and recording the product of the average value and the variance as the printing difference;
and calculating a performance evaluation value according to the gain effect and the printing difference, and evaluating the corona effect of the corona machine based on the performance evaluation value.
2. The method according to claim 1, wherein the material comprises silicone, rubber or plastic.
3. The corona effect evaluation method of claim 1, wherein the data processing comprises normalizing each element in the gray neighborhood difference matrix.
4. The corona effect evaluation method of claim 1, wherein the performance evaluation value is a ratio of the gain effect to a print variation.
5. The corona effect evaluation method of claim 1, wherein the corona effect of the corona machine is evaluated by: and comparing the performance evaluation value with an evaluation threshold value, wherein when the performance evaluation value is larger than the evaluation threshold value, the corona effect of the corona machine is good, and when the performance evaluation value is smaller than the evaluation threshold value, the corona effect of the corona machine is poor.
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